
import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
		import torch

		from transformers.generation import (
		    MaxLengthCriteria,
		    MaxNewTokensCriteria,
		    MaxTimeCriteria,
		    StoppingCriteriaList,
		    validate_stopping_criteria,
		)




@require_torch
class 					lowerCamelCase__				(							unittest.TestCase):




	'''simple docstring'''




	def   _lowerCamelCase							(   self						:Union[str, Any]	,  a						:str		)						->					Optional[Any]:
								__UpperCamelCase      :  Any       =   3
								__UpperCamelCase      :  Tuple       =   2_5_0

								__UpperCamelCase      :  str       =   ids_tensor((batch_size, length)	,  a		)
								__UpperCamelCase      :  List[str]       =   torch.ones((batch_size, length)	,  device=a	,  dtype=torch.float		) / length
								return input_ids, scores







	def   _lowerCamelCase							(   self						:List[Any]		)						->					List[str]:
								__UpperCamelCase  ,		__UpperCamelCase      :  int       =   self._get_tensors(5		)

								__UpperCamelCase      :  str       =   StoppingCriteriaList(
								    [
								        MaxLengthCriteria(max_length=1_0		),
								        MaxTimeCriteria(max_time=0.1		),
								    ]		)

								self.assertFalse(criteria(a	,  a		)		)

								__UpperCamelCase  ,		__UpperCamelCase      :  Union[str, Any]       =   self._get_tensors(9		)
								self.assertFalse(criteria(a	,  a		)		)

								__UpperCamelCase  ,		__UpperCamelCase      :  Tuple       =   self._get_tensors(1_0		)
								self.assertTrue(criteria(a	,  a		)		)







	def   _lowerCamelCase							(   self						:str		)						->					Optional[Any]:
								__UpperCamelCase      :  Union[str, Any]       =   MaxLengthCriteria(max_length=1_0		)

								__UpperCamelCase  ,		__UpperCamelCase      :  Dict       =   self._get_tensors(5		)
								self.assertFalse(criteria(a	,  a		)		)

								__UpperCamelCase  ,		__UpperCamelCase      :  List[Any]       =   self._get_tensors(9		)
								self.assertFalse(criteria(a	,  a		)		)

								__UpperCamelCase  ,		__UpperCamelCase      :  Tuple       =   self._get_tensors(1_0		)
								self.assertTrue(criteria(a	,  a		)		)







	def   _lowerCamelCase							(   self						:Any		)						->					Any:
								__UpperCamelCase      :  Dict       =   MaxNewTokensCriteria(start_length=5	,  max_new_tokens=5		)

								__UpperCamelCase  ,		__UpperCamelCase      :  int       =   self._get_tensors(5		)
								self.assertFalse(criteria(a	,  a		)		)

								__UpperCamelCase  ,		__UpperCamelCase      :  List[str]       =   self._get_tensors(9		)
								self.assertFalse(criteria(a	,  a		)		)

								__UpperCamelCase  ,		__UpperCamelCase      :  Any       =   self._get_tensors(1_0		)
								self.assertTrue(criteria(a	,  a		)		)

								__UpperCamelCase      :  Tuple       =   StoppingCriteriaList([criteria]		)
								self.assertEqual(criteria_list.max_length	,  1_0		)







	def   _lowerCamelCase							(   self						:str		)						->					str:
								__UpperCamelCase  ,		__UpperCamelCase      :  Union[str, Any]       =   self._get_tensors(5		)

								__UpperCamelCase      :  Optional[Any]       =   MaxTimeCriteria(max_time=0.1		)
								self.assertFalse(criteria(a	,  a		)		)

								__UpperCamelCase      :  List[Any]       =   MaxTimeCriteria(max_time=0.1	,  initial_timestamp=time.time() - 0.2		)
								self.assertTrue(criteria(a	,  a		)		)







	def   _lowerCamelCase							(   self						:str		)						->					Union[str, Any]:
								validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(1_0		)]		)	,  1_0		)

								with self.assertWarns(a		):
															validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(1_0		)]		)	,  1_1		)

								__UpperCamelCase      :  List[Any]       =   validate_stopping_criteria(StoppingCriteriaList()	,  1_1		)

								self.assertEqual(len(a		)	,  1		)

from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def   _lowerCamelCase							(   self						:Tuple	,  a						:float		)						->					float:
								return 0.0




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     np.ndarray		,    _lowerCamelCase					:     int)   ->       tuple[int | float, int | float]:






							'''simple docstring'''





							__UpperCamelCase      :  List[str]       =   min([-20, np.min(fft_results[1 : samplerate // 2 - 1])])
							__UpperCamelCase      :  Any       =   max([20, np.max(fft_results[1 : samplerate // 2 - 1])])
							return lowest, highest




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     FilterType		,    _lowerCamelCase					:     int)   ->       None:






							'''simple docstring'''





							__UpperCamelCase      :  List[str]       =   512
							__UpperCamelCase      :  List[Any]       =   [1] + [0] * (size - 1)
							__UpperCamelCase      :  List[Any]       =   [filter_type.process(_lowerCamelCase) for item in inputs]

							__UpperCamelCase      :  Optional[Any]       =   [0] * (samplerate - size)  # zero-padding
							outputs += filler
							__UpperCamelCase      :  Optional[int]       =   np.abs(np.fft.fft(_lowerCamelCase))
							__UpperCamelCase      :  Optional[int]       =   20 * np.logaa(_lowerCamelCase)

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24		,    samplerate / 2 - 1)
							plt.xlabel("Frequency (Hz)")
							plt.xscale("log")

							# Display within reasonable bounds
							__UpperCamelCase      :  Optional[Any]       =   get_bounds(_lowerCamelCase		,    _lowerCamelCase)
							plt.ylim(max([-80, bounds[0]])		,    min([80, bounds[1]]))
							plt.ylabel("Gain (dB)")

							plt.plot(_lowerCamelCase)
							plt.show()




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     FilterType		,    _lowerCamelCase					:     int)   ->       None:






							'''simple docstring'''





							__UpperCamelCase      :  Any       =   512
							__UpperCamelCase      :  Dict       =   [1] + [0] * (size - 1)
							__UpperCamelCase      :  Tuple       =   [filter_type.process(_lowerCamelCase) for item in inputs]

							__UpperCamelCase      :  Dict       =   [0] * (samplerate - size)  # zero-padding
							outputs += filler
							__UpperCamelCase      :  Optional[int]       =   np.angle(np.fft.fft(_lowerCamelCase))

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24		,    samplerate / 2 - 1)
							plt.xlabel("Frequency (Hz)")
							plt.xscale("log")

							plt.ylim(-2 * pi		,    2 * pi)
							plt.ylabel("Phase shift (Radians)")
							plt.plot(np.unwrap(_lowerCamelCase		,    -2 * pi))
							plt.show()